#virality — Public Fediverse posts

#Business #Approaches
Your audience isn’t that valuable · The “low virality, high authority” content strategy https://ilo.im/16f1yg

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#Audience #Content #ContentStrategy #Marketing #SocialMedia #Virality #Authority #Trust #LongTail

Virality Is a Business Model

By Cliff Potts, CSO, and Editor-in-Chief of WPS News
Baybay City, Leyte, Philippines — May 26, 2026

Reporting

On Facebook, viral content is often described as accidental or organic. In practice, virality is not a side effect. It is a feature.

Facebook is designed to identify, amplify, and recycle content that spreads quickly. Posts that move rapidly through networks—especially videos—are rewarded with additional visibility. This process increases user activity, which in turn increases advertising value.

Virality is not evidence of importance. It is evidence that content performs well inside Facebook’s engagement system.

Structural Context

Facebook measures success by time spent, interactions generated, and repeat exposure. Content that spreads quickly keeps users active and attracts advertisers seeking large audiences.

To support this, Facebook’s systems:

• boost posts that receive early engagement,
• promote content that encourages resharing,
• and deprioritize material that resolves issues or discourages further interaction.

The faster content spreads, the more valuable it becomes to the platform—regardless of whether it is accurate, current, or useful.

Facebook Is Not the Internet

This distinction must be repeated clearly:

Facebook is not the internet.
It is a closed system that selects what users see based on business incentives.

On the open internet, information competes on relevance, timeliness, and credibility. On Facebook, information competes on shareability. What spreads best is what survives.

When Filipinos rely on Facebook as their primary source of information, they are not seeing a representative view of events. They are seeing what travels well inside a commercial network.

Viral Videos and the Illusion of Urgency

Video content is especially effective in this system. Videos autoplay, generate longer viewing times, and provoke emotional reactions. As a result, they are frequently recycled.

A video recorded months earlier can resurface repeatedly as different pages repost it with new captions. Context is often removed. Dates are rarely emphasized. To users, the material appears urgent and current even when it is not.

Each resharing creates the impression that “everyone is talking about this now,” even if the underlying event is long past.

Analysis

Virality rewards repetition, not resolution. Content that spreads widely is kept in circulation because it continues to generate reactions. Content that explains outcomes, corrections, or follow-ups tends to slow engagement and is quietly deprioritized.

This creates a feedback loop:

• old material reappears as new,
• emotional responses are reactivated,
• and public attention remains fixed on unresolved narratives.

When Facebook functions as a dominant information channel, this loop distorts how time, relevance, and importance are perceived.

Practical Implications

Breaking the influence of viral distortion requires restraint rather than participation.

Practical steps include:

• Pausing before resharing viral videos.
• Checking when and where content was originally recorded.
• Searching outside Facebook to confirm whether an issue is current.
• Understanding that high share counts reflect performance, not urgency.

Choosing not to reshare is often more informative than amplifying outdated material.

Conclusion

Virality on Facebook is not a measure of public need. It is a measure of commercial success.

Facebook is not the internet.
Treating viral content as inherently important allows old, incomplete, and misleading information to dominate attention. Recognizing virality as a business model is essential to restoring perspective in the Philippine information environment.

For more social commentary, please see Occupy 2.5 at https://Occupy25.com

Archived as part of the WPS News Monthly Brief Series (Amazon).

References
Meta Platforms, Inc. (2023). Transparency Center reports.
Independent academic and investigative reporting on viral media and engagement-based distribution systems.

"In the new algorithm, that mechanism is vastly demoted: reposts - like every post - need to go through the retrieval and ranking stage mentioned above, so a repost from a big account is a long way from the boost it used to be.

This is especially brutal for low-effort quote tweets, which used to function as cheap amplification: now they often can't even clear the retrieval stage - they simply don't contain enough novel semantic content for the system to match them to anyone's interests.

So, putting it all together, the reach collapse comes from many forces stacking at once:
- Auto-translate makes your posts compete for attention against an order of magnitude more content
- The retrieval stage matches posts by topic, not by who follows you
- The ranking stage scores purely on predicted engagement with no weight for credibility, expertise, or track record
- The bloom filter narrows every post's window to one strong shot
- The diversity scorer penalizes prolific posting
- Reposts no longer carry much distribution power

Each of these alone would dent your reach. Combined, they amount to a complete reset: your audience that you built painstakingly over years basically doesn't matter much anymore, and it's much - much - harder to stand out even if you're a big account.

People structurally rewarded by this algorithm are folks who:
- Post visually (videos/images)
- Post on globally popular topics because they clear the retrieval stage easily
- Provoke strong emotional reactions - likes, replies, reposts
- Don't care about accuracy or seriousness because the algorithm doesn't measure it
- Don't care about their existing audience because every post is judged in isolation anyway

In short this new algorithm, like so many on social media, is all about maximizing whether people will engage with something - not about whether they should."

https://x.com/RnaudBertrand/status/2058450505630716013

#SocialMedia #Engagement #Twitter #Virality

Is Flappy Bird a good game?

https://piefed.blahaj.zone/c/games/p/641975/is-flappy-bird-a-good-game

I saw this on Mastodon and almost had a stroke.

@davidgerard wrote:

“Most of the AI coding claims are conveniently nondisprovable. What studies there are show it not helping coding at all, or making it worse

But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.

These guys had one good experience with the bot, they got one-shotted, and now if you say “perhaps the bot is not all that” they act like you’re trying to take their cocaine away.”

First, the term is falsifiable, and proving propositions about algorithms (i.e., code) is part of what I do for a living. Mathematically human-written code and AI-written code can be tested, which means you can falsify propositions about them. You would test them the same way.

There is no intrinsic mathematical distinction between code written by a person and code produced by an AI system. In both cases, the result is a formal program made of logic and structure. In principle, the same testing techniques can be applied to each. If it were really nondisprovable, you could not test to see what is generated by a human and what is generated by AI. But you can test it. Studies have found that AI-generated code tends to exhibit a higher frequency of certain types of defects. So, reviewers and testers know what logic flaws and security weaknesses to look for. This would not be the case if it were nondisprovable.

You can study this from datasets where the source of the code is known. You can use open-source pull requests identified as AI-assisted versus those written without such tools. You then evaluate both groups using the same industry-standard analysis tools: static analyzers, complexity metrics, security scanners, and defect classification systems. These tools flag bugs, vulnerabilities, performance issues, and maintainability concerns. They do so in a consistent way across samples.

A widely cited analysis of 470 real pull requests reported that AI-generated contributions contained roughly 1.7 times as many issues on average as human-written ones. The difference included a higher number of critical and major defects. It also included more logic and security-related problems. Because these findings rely on standard measurement tools — counting defects, grading severity, and comparing issue rates — the results are grounded in observable data. Again, I am making a point here. It’s testable and therefore disproveable.

This is a good paper that goes into it:

In this paper, we present a large-scale comparison of code authored by human developers and three state-of-the-art LLMs, i.e., ChatGPT, DeepSeek-Coder, and Qwen-Coder, on multiple dimensions of software quality: code defects, security vulnerabilities, and structural complexity. Our evaluation spans over 500k code samples in two widely used languages, Python and Java, classifying defects via Orthogonal Defect Classification and security vulnerabilities using the Common Weakness Enumeration. We find that AI-generated code is generally simpler and more repetitive, yet more prone to unused constructs and hardcoded debugging, while human-written code exhibits greater structural complexity and a higher concentration of maintainability issues. Notably, AI-generated code also contains more high-risk security vulnerabilities. These findings highlight the distinct defect profiles of AI- and human-authored code and underscore the need for specialized quality assurance practices in AI-assisted programming.

https://arxiv.org/abs/2508.21634

Something I’ve started to notice about a lot of the content on social media platforms is that most of the posts people are liking, sharing, and memetically mutating—and then spreading virally—usually don’t include any citations, sources, or receipts. It’s often just some out-of-context screenshot with no reference link or actual sources.

A lot of the anti-AI content is not genuine critique. It’s often misinformation, but people who hate AI don’t question it or ask for sources because it aligns with their biases. The propaganda on social media has gotten so bad that anything other than heavily curated and vetted feeds is pretty much useless, and it’s filled with all sorts of memetic contagions with nasty hooks that are optimized for you algorithmically. I am at the point where I will disregard anything that is not followed up with a source. Period. It is all optimized to persuade, coerce, or piss you off. I am only writing about this because this I’m actually able to contribute genuine information about the topic.

That they said symbolic propositions written by AI agents (i.e., code) are non-disprovable because they were written by AI boggles my mind. It’s like saying that an article written in English by AI is not English because AI generated it. It might be a bad piece of text, but it’s syntactically, semantically, and grammatically English.

Basically, any string of data can be represented in a base-2 system, where it can be interpreted as bits (0s and 1s). Those bits can be used as the basis for symbolic reasoning. In formal propositional logic, a proposition is a sequence of symbols constructed according to strict syntax rules (atomic variables plus logical connectives). Under a given semantics, it is assigned exactly one truth value (true or false) in a two-valued logic system.

They are essentially saying that code written by AI is not binary, isn’t symbolically logical at all, and cannot be evaluated as true or false by implying it is nondisproveable. At the lowest level, compiled code consists of binary machine instructions that a processor executes. At higher levels, source code is written in symbolic syntax that humans and tools use to express logic and structure. You can also translate parts of code into formal logic expressions. For example, conditions and assertions in a program can be modeled as Boolean formulas. Tools like SAT/SMT solvers or symbolic execution engines check those formulas for satisfiability or correctness. It blows my mind how confidently people talk about things they do not understand.

Furthermore that they don’t realize the projection is wild to me.

@davidgerard wrote:

“But SO MANY LOUD ANECDOTES! Trust me my friend, I am the most efficient coder in the land now. No, you can’t see it. No, I didn’t measure. But if you don’t believe me, you are clearly a fool.”

They are presenting a story—i.e., saying that the studies are not disprovable—and accusing computer scientists of using anecdotal evidence without actually providing evidence to support this, while expecting people to take it prima facie. You’re doing what you are accusing others of doing.

It comes down to this: they feel that people ought not to use AI, so they are tacitly committed to a future in which people do not use AI. For example, a major argument against AI is the damage it is doing to resources, which is driving up the prices of computer components, as well as the ecological harm it causes. They feel justified in lying and misinforming others if it achieves the outcome they want—people not using AI because it is bad for the environment. That is a very strong point, but most people don’t care about that, which is why they lie about things people would care about.

It’s corrupt. And what’s really scary is that people don’t recognize when they are part of corruption or a corrupt conspiracy to misinform. Well, they recognize it when they see the other side doing it, that is. No one is more dangerous than people who feel righteous in what they are doing.

Users Are Too Dependent on Centralized Techno-Fascist Corporate Structure to Ever Leave Discord

I’m watching people scatter into countless real-time chat alternatives to Discord after Discord started pulling the age-verification and age-gating card.

https://techcrunch.com/2026/02/09/discord-to-roll-out-age-verification-next-month-for-full-access-to-its-platform/

It’s very frustrating because people are entirely missing the point of a community and how social networks work. Real-time platforms and social media networks only work well when a large number of people share the same space at the same time. If everyone creates separate servers or competing apps, the result is fragmentation that makes it unviable.

One reason why Bluesky became so successful is the invitation and starter-pack move. It essentially allowed people to move collectively as cliques. Bluesky used invitations and starter packs to move groups of friends together. This kept communities intact. Moving as cliques preserves network structure, whereas random scattering does not. People aren’t do not seem to intend to move as cliques or subgraphs of networks off of Discord. And the whole reason people were on Discord was to host their communities, so an alternative becomes pointless if your community doesn’t remain intact.

Instead of an active, strongly connected, possibly distributed network, you get dozens of small pockets. I am referring to a potential distributed network rather than a single centralized platform, because Matrix is an example of a decentralized chat protocol. Not all alternatives have to be centralized like Discord. Technically, many older chat protocols, such as XMPP and IRC, are examples of federated real-time synchronous messaging. They allowed communication between users on different, independently operated servers. Federation means that multiple servers can interconnect so that users from separate networks can exchange messages with one another seamlessly.

Decentralized alternatives would not be a problem if people moved to the same distributed network as cohesive groups. However, what I am seeing is that people move in disconnected and stochastic ways to entirely separate distributed networks, so communities are not kept intact. For example, when people move to XMPP servers or Matrix servers, it bifurcates and disconnects social networks. Notice I said XMPP or Matrix, which logically means people are on Matrix but not XMPP, or they are on XMPP but not Matrix. That implies a person would need to be on both Matrix and XMPP to speak to their original community from Discord if it split down the middle. To synchronize conversations in chats, there would need to be a bridge. It’s a pretty complicated solution.

The likely outcome is that people will remain on the dominant platform because of its scale and structure. The deeper irony is that while people may want independence from corporate platforms, they often struggle to organize effectively without the centralized structure those platforms provide. They’ve become so dependent on corporate structures to support their communities that they have no clue how to organize their own social networks in a sustainable way.

I’ve always been an internet nerd, but most of my social life has been offline. I view my interactions with the social app layer of the internet as a game, so losing that domain of the Internet is not devastating to me.

I’ll give you an example. This is a WordPress site. You hear this insincere nostalgia from Millennials and Gen X for a simulacrum that never was, especially concerning forums. Check this out: when you go into the plugin installation section of WordPress, this is on the second row you see:

https://bbpress.org/

That means any WordPress site has the capability to host a forum. They’re nostalgic for a setup where you can use a simple install script on any hosting service to install WordPress. After that, you can then just add a plugin to turn it into a forum. Hell, they can do this on WordPress.com if they don’t want to self-host.

You can make a forum, but no one will use it because they’d rather use a centralized platform like Reddit. Users have become so dependent on corporations to structure and organize communities that they can’t do it themselves. It’s sort of like the cognitive debt that accrues when people outsource their thinking to AI.

The issue is not that forums are hard to host or create; rather, the issue is that people have become so dependent on centralized corporate structures that they can’t maintain or organize their own communities, which is why everyone ends up on Reddit or Discord. A reason I keep hearing for why people don’t want to leave Discord is that it’s hard to recreate the community structure that Discord’s features provide. They claim that they want independence from corporate platforms, but rely on the centralized structure those platforms provide to function socially.

People say they want decentralized freedom, but in practice they depend on centralized platforms to maintain social cohesion. Stochastically scattering to the digital winds of the noosphere destroys the very communities they’re trying to preserve.

@cloudskater wrote:

Some instances are run by bad people. Hell, a few projects like Lemmy and Matrix are DEVELOPED by assholes, but the FLOSS and federated nature of these platforms allows us to bypass/fork them and create healthy spaces outside their reach.

Nope, that is actually what is killing the fediverse. I just explained here:

The issue is the divergence in semantic interpretation that emerges at the interpretation layer. ActivityPub standardizes message delivery and defines common activity types. However, it leaves extension semantics and application-layer policy decisions to individual implementations. Servers may introduce custom JSON-LD namespaces and enforce local behaviors, such as reply restrictions, while remaining protocol-compliant. But, the noise created by divergences are problematic, because it creates unexpected, unintended, and unpredictable behavior.

Divergence appears when implementations rely on non-normative metadata and assume reciprocal handling to preserve a consistent user experience. Behavioral alignment then varies. Syntactic exchange succeeds, but behavioral consistency is not guaranteed. Though instances continue to federate at the transport level, policy semantics and processing logic differ across deployments. Those differences produce inconsistent experiences and results between implementations.

That leads to fragmentation, specifically semantic or behavioral fragmentation and an inconsistent user experiences. ActivityPub ensures syntactic interoperability, but semantic interoperability (everyone interprets and enforces rules the same way) varies. This creates a system that is federated at the transport level yet fragmented in behavior and expectations across implementations. It is funny how the thing that the fediverse touted has made the entire thing very brittle. ActivityPub technically federates correctly, but semantically falls apart once servers start adding their own behavioral rules.

https://neon-blue-demon-wyrm.x10.network/archives/16932

FYI, I’m not doing culture wars or political debates. I’m just saying this idea of “forking away” from them is literally breaking the fediverse’s distributed network and creating all kinds of issues with semantic interoperability. Yes, federation is still happening at the delivery level, but the semantic issues are out of fucking control. You are a federation by the very sheer skin of your teeth.

The reason why developers are leaving the fediverse is because you folks don’t take criticism. You respond to criticism with — I’m being so serious right now — political manifestos and harassing developers. ActivityPub developers and authors oversold you folks on the capabilities of ActivityStreams. They flat-out lied to y’all.

↬bark.lgbt/@cloudskater/116080965694723006

ActivityPub Server’s Custom Reply‑Control Extensions Undermine Federation

It seems like Activitbypub developers are extending ActivityPub with optional metadata to fix a lot of its issues, but that is still problematic. Trying to add moderation tools and user control to threads seems to be the ongoing battle. I am fascinated by dumpster fires, so I’ve started looking at the ActivityPub protocol in detail. I tend to become fascinated with things that are going down in flames.

As a brief recap of the problem:

So, one of the very popular features on Bluesky—also popular on Twitter—is the ability to select who can reply to a post. A major issue in the Fediverse is the inability to decide who can reply, and once you block someone, their harassing reply is still there. I honestly thought it was simply a case of them choosing not to add or address it for cultural reasons. What is clear from that thread is that they were always aware that the ActivityPub protocol and most Fediverse implementations don’t provide a universal way to control reply visibility or enforce blocks across instances.

An ActivityPub server that has reply control is GoToSocial. ActivityPub, as defined by the W3C specification, standardizes how servers federate activities. It defines actors, inboxes, outboxes, and activity types (Create, Follow, Like, Announce, etc.) expressed using ActivityStreams 2.0. It also specifies delivery mechanics (including how a Create activity reaches another server’s inbox) and how collections behave.

The specification does not include interaction policy semantics such as “only followers may reply” or “replies require manual approval.” There is no field in the normative vocabulary requiring conforming servers to enforce reply permissions. That category of rule is outside the protocol’s defined contract.

GoToSocial implements reply controls through what it calls interaction policies. These appear as additional properties on ActivityStreams objects using a custom JSON-LD namespace controlled by the GoToSocial project.

JSON-LD permits additional namespaced terms. This means the document remains structurally valid ActivityStreams and federates normally. The meaning of those custom fields, however, comes from GoToSocial’s own documentation and implementation. Other servers can ignore them without violating ActivityPub because they are not part of the interoperable core vocabulary.

Enforcement occurs locally. When a remote server sends a reply—a Create activity whose object references another via inReplyTo—ActivityPub governs delivery, not acceptance criteria. Whether the receiving server checks a reply policy, rejects the activity, queues it, or displays it is determined in the server’s inbox-processing code. The decision to accept, display, or require approval happens after successful protocol-level delivery. This behavior belongs to the application layer.

These are server-side features layered on top of ActivityPub’s transport and data model that are not actually part of ActivityPub. The protocol ensures standardized delivery of activities; however, the server implementation defines additional constraints and user-facing behavior. Two GoToSocial instances may both recognize and act on the same extension fields. However, a different implementation, such as Mastodon, has no obligation under the specification to interpret or enforce GoToSocial’s interactionPolicy properties. These fields function as extension metadata rather than protocol requirements.

The semantics of GoToSocial are not part of the specification’s defined vocabulary and processing rules for ActivityPub. They no longer operate purely at the protocol layer; it has become an application-layer contract implemented by specific servers.

Let’s use the AT Protocol as an example. Bluesky’s direct messages (DMs) are not currently part of the AT Protocol (ATProto). The AT Protocol has nothing that specifies anything for DMs, so DMs are not part of the AT Protocol. The AT Protocol was designed to handle public social interactions, but it does not define private or encrypted messaging. Bluesky implemented DMs at the application level, outside of the core protocol. DMs are centralized and stored on Bluesky’s servers. What is happening with servers like GoToSocial is sort of like that. The difference is that the AT Protocol was designed for different app views; ActivityPub was not.

The issue is the divergence in semantic interpretation that emerges at the interpretation layer. ActivityPub standardizes message delivery and defines common activity types. However, it leaves extension semantics and application-layer policy decisions to individual implementations. Servers may introduce custom JSON-LD namespaces and enforce local behaviors, such as reply restrictions, while remaining protocol-compliant. But, the noise created by divergences are problematic, because it creates unexpected, unintended, and unpredictable behavior.

Divergence appears when implementations rely on non-normative metadata and assume reciprocal handling to preserve a consistent user experience. Behavioral alignment then varies. Syntactic exchange succeeds, but behavioral consistency is not guaranteed. Though instances continue to federate at the transport level, policy semantics and processing logic differ across deployments. Those differences produce inconsistent experiences and results between implementations.

That leads to fragmentation, specifically semantic or behavioral fragmentation and an inconsistent user experiences. ActivityPub ensures syntactic interoperability, but semantic interoperability (everyone interprets and enforces rules the same way) varies. This creates a system that is federated at the transport level yet fragmented in behavior and expectations across implementations. It is funny how the thing that the fediverse touted has made the entire thing very brittle. ActivityPub technically federates correctly, but semantically falls apart once servers start adding their own behavioral rules.

Stepping Back From Social Media To Read a Book

I’m taking a break. After spending like two years in the worst parts of the Internet modeling the memetic spread of conspiracy-driven behavioral patterns and developing social media software as a side hustle, I think I’m going to take a step back and… I don’t know… maybe read a book? lol.

I’m a Computational Biologist who pretty much studies the memetics of conspiracy theories and how they act as another vector/epidemiological layer. I’ve also been working on various contracts for social media development stuff. Working on the shit I’ve been working on for years forces you to see the worst parts of people that they split off. It makes you hate everyone — and I mean everyone.

Your BlueSky Feed Is Porn You Didn’t Ask For Because Your Friends Are Gooners With a Severe Porn Addiction

A common complaint I see people make on Bluesky is: why am I being served so much porn or things I am not interested in? They will incorrectly believe that the algorithm is broken. It’s not broken. You didn’t know the people you knew as well as you thought you did. Porn addiction is a thing, and porn addiction is especially common with weebs. You’re seeing deranged shit because people you follow have porn addictions and are into deranged shit. So, though you may not be consuming porn, people in your network are. That activity kicks into your feeds.

The issue I have with that is that it essentially normalizes being sex pests in a space on the Internet. That sets the expectation that it is good—attractive, even—to act like that elsewhere. That expectation alienates relationships. Bluesky creates a cultural space that offers an unrealistic, bizarre representation of social relationships, which isolates and alienates the users who stay on there consuming erotica and porn like they do.

So, user repos in Bluesky have a property for likes. Bluesky’s underlying AT Protocol stores likes as first-class structured records in each user’s AT Protocol repository. In the AT Protocol lexicon, a like is an app.bsky.feed.like record type. Unlike a simple boolean flag on a post, it is its own record with a creation timestamp and a subject field that holds a strong reference to the liked record.

That strong reference is composed of an AT-URI and a CID. The AT-URI identifies the exact record in the network by DID, collection, and record key. The CID is a cryptographic content identifier that uniquely identifies the exact content of that liked record.

These like records exist under the app.bsky.feed.like namespace in the user’s repo. Bluesky’s repo model is built so that these repos are hosted on a user’s Personal Data Server and are publicly readable through the AT Protocol APIs. Because of that, the like record and its fields can be fetched, indexed, and used by any client or service that can query the protocol.

The protocol exposes operations like getLikes. This returns all of the like records tied to a particular subject’s AT-URI and CID. It also exposes getActorLikes. This returns all of the subject references a given actor has liked. Those API calls return structured like objects with timestamps and subject references directly from the public repository data.

Various feeds hosted by different PDSs use the likes property to construct the feeds that you see. Since the likes of people you follow are included in your social graph, along with your own likes, you’re going to get served the porn they are consuming. Because likes are public and anyone can write an algorithm to see everyone’s likes, you can clearly see just how much porn people are consuming.

Honestly, what started to turn my stomach about the people on Bluesky is how they behave across different contexts. If you look through the records of the posts they interact with, you’ll see them engaging with political posts in the replies like a normal person. Then, when you look through their AT Protocol records, you see hours and hours of them interacting with every kind of porn imaginable. I am not exaggerating. Hours of likes for porn posts within 1–10 minutes of each other. Am I sex-negative? A prude? No, this site is filled with furry, gay bara porn, lol. You can have a drink without being an alcoholic. The problem with these people is like people who can’t have one drink without drinking the whole fucking day; they can’t consume porn in healthy ways.

I think people assume that their feed is customized for them and based on their likes. No—feeds are generalized based on what everyone likes and then served to your subgraph. It’s not just about who you follow; it’s about who they follow. So if you follow someone who follows a lot of people with porn addictions, you will see porn. Bluesky isn’t weighting the algorithm to do this. Basically, it’s the people in your social network with furry, hentai, or trans porn addictions who are driving it.

BlueSky’s Solution To Moderating Is Moderating Without Moderating via Social Proximity

I have noticed a lot of people are confused about why some posts don’t show up on threads, though they are not labeled by the moderation layer. Bluesky has begun using what it calls social neighborhoods (or network proximity) as a ranking signal for replies in threads. Replies from people who are closer to you in the social graph, accounts you follow, interact with, or share mutual connections with, are prioritized and shown more prominently. Replies from accounts that are farther away in that network are down-ranked. They are pushed far down the thread or placed behind “hidden replies.”

Each person gets their own unique view of a thread based on their social graph. It creates the impression that replies from distant users simply don’t exist. This is true even though they’re still technically public and viewable if you expand the thread or adjust filters. Bluesky is explicitly using features of subgraphs to moderate without moderating. Their reasoning is that if you can’t see each other, you can’t harass each other. Ergo, there is nothing to moderate.

Bluesky mentions that here:

https://bsky.social/about/blog/10-31-2025-building-healthier-social-media-update

As a digression, I’m not going to lie: I really enjoyed working on software built on the AT protocol, but their fucking users are so goddamn weird. It’s sort of like enjoying building houses, but hating every single person who moves into them. But, you don’t have to deal with them because you’re just the contractor. That is how I feel about Bluesky. I hate the people. I really like the protocol and infrastructure.

I sort of am a sadist who does enjoy drama, so I do get schadenfreude from people with social media addictions and parasocial fixations who reply to random people on Bluesky, because they don’t realize their replies are disconnected from the author’s thread unless that person is within their network. They aren’t part of the conversation they think they are. They’re algorithmically isolated from everyone else. Their replies aren’t viewable from the author’s thread because of how Bluesky handles social neighborhoods.

Bluesky’s idea of social neighborhoods is about grouping users into overlapping clusters based on real interaction patterns rather than just the follow graph. Unlike Twitter, it does not treat the network as one big public square. Instead, it models networks of “social neighborhoods” made up of people you follow, people who follow you, people you frequently interact with, and people who are closely connected to those groups. They’re soft, probabilistic groupings rather than strict labels.

Everyone does not see the same replies. Bluesky is being a bit vague with “hidden.” Hidden means your reply is still anchored to the thread and can be expanded. There is another way Bluesky can handle this. Bluesky uses social neighborhoods to judge contextual relevance. Replies from people inside or near your social neighborhood are more likely to be shown inline with a thread, expanded by default, or served in feeds. Replies from outside your neighborhood are still public and still indexed, but they’re treated as lower-context contributions.

Basically, if you reply to a thread, you will see it anchored to the conversation, and everyone will see it in search results, as a hashtag, or from your profile, but it will not be accessible via the thread of the person you were replying to. It is like shadow-banning people from threads unless they are strongly networked.

Because people have not been working with the AT Protocol like I have, they assume they are shadow-banned across the entire Bluesky app view. No—everyone is automatically shadow-banned from everyone else unless they are within the same social neighborhood. In other words, you are not part of the conversation you think you are joining because you are not part of their social group.

Your replies will appear in profiles, hashtag feeds, or search results without being visually anchored to the full thread. Discovery impressions are neighborhood-agnostic: they serve content because it matches a query, tag, or activity stream. Once the reply is shown, the app then decides whether it’s worth pulling in the rest of the conversation for you. If the original author and most participants fall outside your neighborhood, Bluesky often chooses not to expand that context automatically.

Bluesky really is trying to avoid having to moderate, so this is their solution. Instead of banning or issuing takedown labels to DIDs, the system lets replies exist everywhere, but not in that particular instance of the thread.

I find this ironic because a large reason why many people are staying on Bluesky and not moving to the fediverse—thank God, because I do not want them there—is discoverability, virality, and engagement.

In case anyone is asking how I know so much about how these algorithms work: I was a consultant on a lot of these types of algorithms, so I certainly hope I’d know how they work, lol. No, you get no more details about the work I’ve done. I have no hand in the algorithm Bluesky is using, but I have proposed and implemented that type of algorithm before.

I have an interest in noetics and the noosphere. A large amount of my ontological work is an extension of my attempts to model domains that have no spatial or temporal coordinates. The question is how do you generalize a metric space that has no physically, spatial properties. I went to school to try to formalize those ideas. Turns out they’re rather useful for digital social networks, too. The ontological analog to spatial distance, when you have no space, is a graph of similarities.

Astroturfing Is Pretty Pointless When Social Subgraphs Are Fragmented (e.g., the Fediverse)

I am seeing astroturfing in the fediverse again, by AT Protocol developers implicitly trying to shill their products. I think it is stochastic behavior by developers with too much time on their hands. Honestly, I do not care. I like the people on ActivityPub more, but I like the AT Protocol better, and I have developed for both. Astroturfing on ActivityPub networks is fascinating to me because it is so pointless.

I am actually a Computational Biologist and Computer Scientist whose specialty is combinatorics, social graphs, graph theory, etc. Specifically, I use this to create epidemiological models for the memetic layer of human behaviors that act as vectors for diseases, using the SIRS model. I do not just study germs; I study human behaviors.

The models I construct extend into a “memetic layer,” in which beliefs, norms, and behaviors (such as risk-taking, compliance with public health measures, or susceptibility to misinformation) spread contagiously through social networks. These behaviors function as vectors that modulate biological transmission rates. As a result, the spread of ideas can accelerate, dampen, or reshape the spread of disease. By running computational simulations and agent-based models on these graphs, I study how network structure, influential nodes, clustering, and platform-specific dynamics affect behavioral contagion. I also examine how these factors influence epidemiological outcomes.

To say it very concisely, I study how the spread of bat-shit insane beliefs, shit posts, and memes influences whether or not there is a measles outbreak in Texas. Ironically, this is an evolution of my studying semiotics, memetics, and chaos magick in high school. I got a job where I can use occult, anarchist techniques professionally.

I think a large reason why I do not care about astroturfing in the fediverse is that it’s so pointless, lol. Astroturfing to manipulate the narrative would actually work better on Bluesky to keep people there than trying to recruit from the fediverse. Furthermore, big instances are relatively small. Some people on Bluesky have follower lists larger than an entire large instance in the fediverse.

Within ActivityPub networks, astroturfing rarely propagates far, because whether information spreads depends on properties of the social graph itself. Dense connectivity, short paths between communities, and a sufficient number of cross-cutting ties support diffusion. ActivityPub’s architecture tends to produce graphs that are fragmented and highly modular. This limits the reach of coordinated activity.

ActivityPub is a system where each instance maintains its own local user graph and exchanges activities through inboxes and outboxes. This makes it autonomous and decentralized. The network consists of loosely connected subgraphs. Cross-instance edges appear only through explicit follow relationships. The ActivityPub protocol does not provide a shared or complete view of the network. Measurements of the fediverse consistently show uneven connectivity between instances, clustering at the instance level, and relatively long effective path lengths across the network. Under these conditions, large cascades are uncommon.

Instance-level clustering means that in ActivityPub networks, users interact much more with others on the same server than with users on different servers. Because each instance has its own local timeline, culture, and moderation, connections form densely within instances and only sparsely across them through explicit follow relationships. This creates a network made up of tightly connected local communities linked by relatively few cross-instance ties, which slows the spread of information beyond its point of origin.

However, with the AT Protocol, global indexing and aggregation are explicitly supported. Relays and indexers can assemble near-complete views of the social graph. Applications built on top of this infrastructure operate over a graph that is denser and easier to traverse. There are fewer structural barriers between communities. The diffusion dynamics change substantially when content can move across the graph without relying on narrow federated paths.

Astroturfing depends on coordinated amplification, typically through tightly synchronized clusters of accounts intended to manufacture visibility. Work on coordinated inauthentic behavior shows that these tactics gain traction when they intersect highly connected regions of the graph or bridge otherwise separate communities. In networks with strong modularity, coordination remains local. ActivityPub’s federation model produces this kind of modularity by default. Coordinated clusters stand out clearly within instances. Their effects remain confined to those local neighborhoods.

Astroturfing on ActivityPub therefore tends to stall on its own because of the underlying graph topology. Without dense inter-instance connectivity or any form of global indexing, coordinated campaigns have a hard time moving beyond the immediate regions where they originate. Systems built on globally indexable social graphs, including those enabled by the AT Protocol, expose a much larger surface for viral spread. Network structure and connectivity account for the divergence where that is independent of moderation, cultural norms, ideology, or intent.

It’s just really funny to me how these stochastic techbro groups waste so many resources. I personally don’t want to go viral, which is why I avoid platforms where I can. The fact that it’s harder to achieve high virality on ActivityPub is exactly why I prefer the fediverse over the Atmosphere. One way to think about it is that you can change the ‘genetics’ of a system with a retrovirus, where memetic entities act as cultural retroviruses to reprogram the cultural loci of a space. That is their end goal. They are trying to hijack cultures memetically. You see this a lot with culture jamming.

Basically, the astroturfing on ActivityPub networks is designed to jam and subvert the culture. But, as I have already said, the topological structure makes memetic virality stall. They cannot achieve that kind of viral spread in the fediverse, which is why I cannot understand why they do this every year.

What 4M posts reveal about going viral on Hacker News

https://hn-ph.vercel.app

#HackerNews #goingviral #HackerNews #socialmedia #trends #virality #dataanalysis

#Business #Cartoons
Chasing the social media algorithm · “What about our actual audience?” https://ilo.im/168nno

_____
#Audience #Algorithm #Virality #Content #ContentStrategy #Website #SocialMedia

How social media is typically portrayed and used: just another spam notification, just another source of toxicity and drama.

Sometimes, I see this as a great way: a valuable virtual space for extracting and/or marketing my own data and information.

#hashtags #socialmedia #marketing #strategy #digital #tips #content #reach #engagement #simple #popular #broad #growth #online #communication #media #business #tech #focus #virality #socialmediamarketing #digitalmarketing #inspiration #learning #strategytips

#Discovery and #Virality 🚀 #Algorithms powered by #AI identify viral trends and surface news faster than humans, amplifying the speed at which stories spread globally.

#Misinformation 🛡️ #AItools help platforms detect #fakenews, flag content, and promote credible sources, but challenges remain in balancing accuracy and freedom of speech.

#Engagement 💬 AI analyses #userbehaviour to optimise headlines, timing, and content types, keeping audiences engaged.

👉 https://SocialMediaNews.EU
👉 @media #news

#Virality on the fedi is weird. My most popular post by far, the one about me not wanting a EU Google or facebook gets a smallish boost and fav wave every few weeks. One is happening right now.

I mean looking at actual viral population data, this checks out 🤣

The rapid lowering of barriers to entry for #attention creation through #disintermediating agencies like #YouTube - which also removed quality filters for content being created - has played a key role in creating the twitchy, #chaotic, #disinformation ridden, #virality driven world described here

RE: https://bsky.app/profile/did:plc:5zca2ola2zxpkw37w4f3wxtu/post/3lsixspelzk24

This weeks #Lingthusiasm was pretty cool. They are talking about word use in #tiktok and there is a lot about #Virality and the #algorithm

If you didn't really understand the importance of a sequential media without the algorithm like #mastodon then you should listen to this to see how other social medium platforms feed hyper-polarization, context collapse and addiction.

https://lingthusiasm.com/post/786832701937303552/lingthusiasm-episode-105-linguistics-of-tiktok

#MastodonTips

When you boost it shares it to your feed and all your followers. Without an algorithm running, boost are what makes content spread and sorta “go viral”. A boost is pretty similar to a retweet in Twitter or share in fb.

The culture on mastodon is to be more generous with boosts, to help posts you like get to more folk.

Boost other new folks especially, help them find their people.

#Boosts #Virality #SocialMedia #MastodonCulture

Explosive #virality is one of the emergent features of a hyperconnected #digitalsociety. It can be enhanced by the incentives and designs of platforms, but is intrinsic to the low friction and dense topology of digital #socialnetworks.

Like so many tech changes, cannot be simply labelled as "bad" or "good". It simply means more people act in a coordinated, synchronized fashion.

*What* their action is is the real issue. Good or bad gets multiplied dramatically.

#digitaltransformation

"Meta CEO Mark Zuckerberg also said in January that the company was removing or dialing back automated systems that reduce the spread of false information. At the same time, Meta is revamping a program that has paid bonuses to creators for content based on views and engagement, potentially pouring accelerant on the kind of false posts it once policed. The new Facebook Content Monetization program is currently invite-only, but Meta plans to make it widely available this year.

The upshot: a likely resurgence of incendiary false stories on Facebook, some of them funded by Meta, according to former professional Facebook hoaxsters and a former Meta data scientist who worked on trust and safety.

ProPublica identified 95 Facebook pages that regularly post made-up headlines designed to draw engagement — and, often, stoke political divisions. The pages, most of which are managed by people overseas, have a total of more than 7.7 million followers.

After a review, Meta said it had removed 81 pages for being managed by fake accounts or misrepresenting themselves as American while posting about politics and social issues. Tracy Clayton, a Meta spokesperson, declined to respond to specific questions, including whether any of the pages were eligible for or enrolled in the company’s viral content payout program.

The pages collected by ProPublica offer a sample of those that could be poised to cash in."

https://www.propublica.org/article/facebook-meta-abandons-fact-checking-boosts-viral-content?utm_campaign=propublica-sprout&utm_content=1740434408&utm_medium=social&utm_source=twitter

#SocialMedia #Facebook #FactChecking #Disinformation #Virality #Misinformation #ContentModeration

Key points :
1. To examine the influence of #news source bias on #affective content and #virality over time, we applied #sentiment analysis to a large #socialmedia dataset over a decade (i.e., ~ 30 M twitter posts from 2011-2020).
2. Biased news sources on both sides produced more arousing negative affective content, and this content was also most viral.
3. Biased news sources may generate " #AffectivePollution ”, engaging users at the expense of their knowledge, well-being, and harmony. (2/3)

Key points from "News source bias and sentiment on social media" :
1. To examine the influence of #news source bias on #affective content and #virality over time, we applied #sentiment analysis to a large social #media dataset over a decade (i.e., ~ 30 million twitter posts from 2011-2020).
2. Biased news sources on both sides produced more high arousal negative affective content, and this content was also most viral.
3. The virality of high arousal negative content increased over a decade, particularly for balanced news sources.
4. Biased news sources may generate " #AffectivePollution ” which engages users at the expense of their knowledge, well-being, and harmony. (2/3)

#AI and the Evolution of #SocialMedia
There is a lot we can learn about social media’s #unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.
1, #Advertising
2. #Surveillance
3. #Virality
4. #LockIn
5. #Monopoly
https://www.schneier.com/blog/archives/2024/03/ai-and-the-evolution-of-social-media.html

Chuck Norris was a karate legend, then an action star, and then... memes. So many memes.
This is the hilarious story of how Chuck Norris Facts melted the internet in 2005 and how its creator is now using AI to shake up the world in even bigger ways

https://www.espn.com/mma/story/_/id/38239284/Chuck-Norris-Facts

#memes #ChuckNorris #SocialMedia #Virality

The Venn diagram of people who unironically “try to go viral” and the people who believe the “pandemic is over”…is a circle.

#COVID #virality #selfawareness